Improving LLM Reliability through Hybrid Abstention and Adaptive Detection

• URL: http://arxiv.org/abs/2602.15391v1
• Date: Tue, 17 Feb 2026 07:00:09 GMT
• Title: Improving LLM Reliability through Hybrid Abstention and Adaptive Detection
• Authors: Ankit Sharma, Nachiket Tapas, Jyotiprakash Patra,
• Abstract summary: Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off.<n>Conventional guardrails based on static rules or fixed confidence thresholds are typically context-insensitive and computationally expensive.<n>We introduce an adaptive abstention system that dynamically adjusts safety thresholds based on real-time contextual signals.
• Score: 1.9495934446083012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off either a strict filtering mechanisms prevent harmful outputs but often block benign queries or a relaxed controls risk unsafe content generation. Conventional guardrails based on static rules or fixed confidence thresholds are typically context-insensitive and computationally expensive, resulting in high latency and degraded user experience. To address these limitations, we introduce an adaptive abstention system that dynamically adjusts safety thresholds based on real-time contextual signals such as domain and user history. The proposed framework integrates a multi-dimensional detection architecture composed of five parallel detectors, combined through a hierarchical cascade mechanism to optimize both speed and precision. The cascade design reduces unnecessary computation by progressively filtering queries, achieving substantial latency improvements compared to non-cascaded models and external guardrail systems. Extensive evaluation on mixed and domain-specific workloads demonstrates significant reductions in false positives, particularly in sensitive domains such as medical advice and creative writing. The system maintains high safety precision and near-perfect recall under strict operating modes. Overall, our context-aware abstention framework effectively balances safety and utility while preserving performance, offering a scalable solution for reliable LLM deployment.

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